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Improving Human Gait Recognition Using Feature Selection

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Advances in Visual Computing (ISVC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8888))

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Abstract

Human gait, a biometric aimed to recognize individuals by the way they walk has recently come to play an increasingly important role in visual surveillance applications. Most of the existing approaches in this area, however, have mostly been evaluated without explicitly considering the most relevant gait features, which might have compromised the performance. In this paper, we have investigated the effect of discarding irrelevant or redundant gait features, by employing Genetic Algorithms (GAs) to select an optimal subset of features, on improving the performance of a gait recognition system. Experimental results on the CASIA dataset demonstrate that the proposed system achieves considerable gait recognition performance.

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Author information

Authors and Affiliations

  1. Department of computer Science and Engineering, University of Louisville, USA

    Faezeh Tafazzoli

  2. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis & Sushil Louis

  3. Department of Software Engineering, King Saud University, Riyadh, Saudi Arabia

    Muhammad Hussain

Authors
  1. Faezeh Tafazzoli
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  2. George Bebis
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  3. Sushil Louis
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  4. Muhammad Hussain
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada at Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The University of Texas at Dallas, 75080, Richardson, TX, USA

    Ryan McMahan

  6. NextGen Interactions, 27604, Raleigh, NC, USA

    Jason Jerald

  7. Indiana University, 46202, Indianapolis, IN, USA

    Hui Zhang

  8. Microsoft Research, 1 Microsoft Way, 98052, Redmond, WA, USA

    Steven M. Drucker

  9. University of Delaware, 19716-2712, Newark, DE, USA

    Chandra Kambhamettu

  10. Intel Corp., 95054, Sata Clara, CA, USA

    Maha El Choubassi

  11. Computer Graphics and Interactive Media Lab, Department of Computer Science, University of Houston, 77004, Houston, TX, USA

    Zhigang Deng

  12. NVIDIA, 34788, Leesburg, FL, USA

    Mark Carlson

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© 2014 Springer International Publishing Switzerland

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Tafazzoli, F., Bebis, G., Louis, S., Hussain, M. (2014). Improving Human Gait Recognition Using Feature Selection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_80

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  • DOI: https://doi.org/10.1007/978-3-319-14364-4_80

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14363-7

  • Online ISBN: 978-3-319-14364-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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